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  • View in gallery

    Percent area of each grid cell covered by (a) natural vegetation, (b) maize, (c) soybean, and (d) other crops based on the MR2008 dataset. Basin boundaries are delineated by bold black lines and labeled in (b).

  • View in gallery

    Sen’s slope trends (1984–2007) in annual mean (a) air temperature, (b) diurnal temperature range, (c) precipitation, (d) relative humidity, (e) incoming solar radiation, and (f) wind speed. Trends that are statistically significant at the 90% confidence level are hatched. The bold black line encompasses the primary study region.

  • View in gallery

    Sen’s slope trends in winter mean (DJF) (a) rainfall, (b) snowfall, and (c) snow depth from 1984 to 2007. Trends that are statistically significant at the 90% confidence level are hatched.

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    Mean seasonal cycle of the surface water balance for the (a) Missouri, (b) Upper Mississippi, (c) Ohio, and (d) Great Lakes basins. Each mark on the lines denotes basin averaged long-term monthly mean (1984–2007) observed precipitation (dotted medium gray with circles) and Agro-IBIS simulated ET (black solid with diamonds), runoff (dotted light gray with inverted triangles), and dW/dt (dotted black with triangles). Error bars indicate the interannual standard deviation (1984–2007).

  • View in gallery

    Mean seasonal cycle of Agro-IBIS simulated cold-season (November–April) snow depth for the four river basins. Each bar denotes the basin-averaged long-term monthly mean (1984–2007) snow depth. Error bars indicate the interannual standard deviation.

  • View in gallery

    Sen’s slope trends in components of the annual mean surface water budget (1984–2007), including (a) observed precipitation and Agro-IBIS simulated (b) ET (plotted as −1 × ET), (c) runoff, and (d) the rate of change in soil water storage (dW/dt). Trends that are statistically significant at the 90% confidence level are hatched.

  • View in gallery

    (a)–(d) As in Fig. 6, but for the winter season (DJF). (e),(f) The trends in observed DJF temperature and incoming solar radiation, respectively.

  • View in gallery

    As in Fig. 7, but for the spring season (MAM).

  • View in gallery

    As in Fig. 7, but for the summer season (JJA).

  • View in gallery

    As in Fig. 7, but for the autumn season (SON).

  • View in gallery

    Sen’s slope trends in the seasonal-mean volumetric water content (0–2.5-m soil depth), as simulated by the Agro-IBIS model for 1984–2007 during (a) DJF, (b) MAM, (c) JJA, and (d) SON.

  • View in gallery

    Trends (mm day−1 decade−1) in (top) annual ET (plotted as −1 × ET) and (bottom) runoff from 1984 to 2007 in the Agro-IBIS (a),(d) natural vegetation, (b),(e) maize, and (c),(f) soybean simulations. Shading with (without) hatching indicates significant negative (positive) trends.

  • View in gallery

    Time series of annual ET (mm) from Livneh (light gray), NLDAS2 (medium gray), and the Agro-IBIS model results (black), averaged over each of the four different river basins.

  • View in gallery

    Time series of annual runoff (mm) from Livneh (light gray), NLDAS2 (medium gray), and the Agro-IBIS model results (black), averaged over each of the four different river basins.

  • View in gallery

    Annual mean Agro-IBIS simulated runoff (gray) and observed annual mean streamflow from USGS (black; converted to basin-average runoff) for the (a) Missouri, (b) Upper Mississippi, (c) Ohio, and (d) southern Great Lakes basins.

  • View in gallery

    The 1984–2007 mean DJF (a) observed precipitation and Agro-IBIS simulated (b) ET (plotted as −1 × ET), (c) runoff, and (d) the rate of change in soil water storage (dW/dt).

  • View in gallery

    As in Fig. A2, but for the MAM season.

  • View in gallery

    As in Fig. A2, but for the JJA season.

  • View in gallery

    As in Fig. A2, but for the SON season.

  • View in gallery

    Sen’s slope trends in SON diurnal temperature range from 1984 to 2007. Cross (line) hatching indicates significant positive (negative) trends.

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Decadal-Scale Changes in the Seasonal Surface Water Balance of the Central United States from 1984 to 2007

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  • 1 National Centre for Earth Observation, Department of Meteorology, University of Reading, Reading, United Kingdom
  • | 2 School of Natural Resources, University of Nebraska–Lincoln, Lincoln, Nebraska
  • | 3 Bureau of Water Quality, Wisconsin Department of Natural Resources, Trout Lake Station, Boulder Junction, Wisconsin
  • | 4 Center for Limnology, University of Wisconsin–Madison, Boulder Junction, Wisconsin
  • | 5 Great Lakes Research Center, Michigan Technological University, Houghton, Michigan
  • | 6 Department of Agronomy, and Nelson Institute Center for Sustainability and the Global Environment, University of Wisconsin–Madison, Madison, Wisconsin
  • | 7 Institute of Surface-Earth System Science, Tianjin University, Tianjin, China
  • | 8 School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia
  • | 9 Department of Soil, Water, and Climate, University of Minnesota, Twin Cities, St. Paul, Minnesota
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Abstract

Variations in climate have important influences on the hydrologic cycle. Observations over the continental United States in recent decades show substantial changes in hydrologically significant variables, such as decreases in cloud cover and increases in solar radiation (i.e., solar brightening), as well as increases in air temperature, changes in wind speed, and seasonal shifts in precipitation rate and rain/snow ratio. Impacts of these changes on the regional water cycle from 1984 to 2007 are evaluated using a terrestrial ecosystem/land surface hydrologic model (Agro-IBIS). Results show an acceleration of various components of the surface water balance in the Upper Mississippi, Missouri, Ohio, and Great Lakes basins over the 24-yr period, but with significant seasonal and spatial complexity. Evapotranspiration (ET) has increased across most of our study domain and seasons. The largest increase is found in fall, when solar brightening trends are also particularly significant. Changes in runoff are characterized by distinct spatial and seasonal variations, with the impact of precipitation often being muted by changes in ET and soil-water storage rate. In snow-dominated regions, such as the northern Great Lakes basin, spring runoff has declined significantly due to warmer air temperatures and an associated decreasing ratio of snow in total precipitation during the cold season. In the northern Missouri basin, runoff shows large increases in all seasons, primarily due to increases in precipitation. The responses to these changes in the regional hydrologic cycle depend on the underlying land cover type—maize, soybean, and natural vegetation. Comparisons are also made with other hydroclimatic time series to place the decadal-scale variability in a longer-term context.

Corresponding author: Bo Dong, bo.dong@reading.ac.uk

Abstract

Variations in climate have important influences on the hydrologic cycle. Observations over the continental United States in recent decades show substantial changes in hydrologically significant variables, such as decreases in cloud cover and increases in solar radiation (i.e., solar brightening), as well as increases in air temperature, changes in wind speed, and seasonal shifts in precipitation rate and rain/snow ratio. Impacts of these changes on the regional water cycle from 1984 to 2007 are evaluated using a terrestrial ecosystem/land surface hydrologic model (Agro-IBIS). Results show an acceleration of various components of the surface water balance in the Upper Mississippi, Missouri, Ohio, and Great Lakes basins over the 24-yr period, but with significant seasonal and spatial complexity. Evapotranspiration (ET) has increased across most of our study domain and seasons. The largest increase is found in fall, when solar brightening trends are also particularly significant. Changes in runoff are characterized by distinct spatial and seasonal variations, with the impact of precipitation often being muted by changes in ET and soil-water storage rate. In snow-dominated regions, such as the northern Great Lakes basin, spring runoff has declined significantly due to warmer air temperatures and an associated decreasing ratio of snow in total precipitation during the cold season. In the northern Missouri basin, runoff shows large increases in all seasons, primarily due to increases in precipitation. The responses to these changes in the regional hydrologic cycle depend on the underlying land cover type—maize, soybean, and natural vegetation. Comparisons are also made with other hydroclimatic time series to place the decadal-scale variability in a longer-term context.

Corresponding author: Bo Dong, bo.dong@reading.ac.uk

1. Introduction

Earth’s climate system and hydrologic cycle are intimately connected. Variations in one lead to changes in the other (e.g., Huntington et al. 2009; Haddeland et al. 2014; Kramer and Soden 2016), which can initiate additional feedbacks throughout the system. These interactions not only define the course of variations in the climate and water cycle but also have profound impacts on regional agriculture, ecosystems, and society. The central United States, home of America’s signature field crop (corn) and one of the most important cash crops (soybean), is an area highly sensitive to these changes and water availability (Hurd et al. 1999). Unanticipated anomalous climatic and hydrologic conditions (both surface and groundwater) in this region can be disruptive or even catastrophic to the region’s agricultural economy and the world’s food security (Glotter and Elliott 2016; Tollerud et al. 2018). As such, there are strong demands for assessing the hydrologic consequences of climate variations in this region in order to better manage water resources and sustain food production.

Evidence of long-term hydroclimatic changes in the United States has been reported in numerous studies (e.g., Karl and Knight 1998; Lins and Slack 1999; Milly et al. 2005; Barnett et al. 2008; IPCC 2013, among others). These changes have been found to be highly nonlinear and characterized by decadal to multidecadal variations. Apart from responses to various anthropogenic forces, such variations are also associated with internal climate variability such as the interdecadal Pacific oscillation [IPO or Pacific decadal oscillation (PDO); Mantua et al. 1997; Deser et al. 2004; Dong and Dai 2015, 2017] and the Atlantic multidecadal oscillation (AMO; Schlesinger and Ramankutty 1994). The phase shift of the IPO around 1976/77 is found to be concurrent with a step increase in annual streamflow in the conterminous United States (McCabe and Wolock 2002). Mo and Lettenmaier (2018) found that most prolonged central U.S. drought occurred in synchrony with cold IPO and warm AMO phases. Since the 1980s, there have been significant changes in climatic variables in North America that depart from their prior, century-long trends. These variables include air temperature, solar radiation, and precipitation (further discussed in section 4a). While many studies have focused on long-term hydrologic trends on time scales longer than 50 years (e.g., Milly and Dunne 2001; Walter et al. 2004; Greve et al. 2014), it is equally important to analyze decadal to multidecadal variations, since century-long trends can mask the magnitude of hydroclimatic variations on shorter time scales.

Impacts on regional water availability from decadal to multidecadal climate variations can be assessed by quantifying changes in the terrestrial surface water budget, including precipitation, evapotranspiration (ET), runoff, and the rate of change in soil water storage (dW/dt). However, because ET is sparsely observed in space and time, geospatial characteristics of variations in regional water availability are extremely difficult to characterize based solely on in situ observations. Recent developments in hydrologic and land surface models (LSMs), which simulate the terrestrial water cycle by accounting for both vegetation and soil effects, offer a method to quantitatively assess surface water balances at various spatial scales (e.g., Lenters et al. 2000; Qian et al. 2007; Alkama et al. 2010; Cai et al. 2014). For more accurate simulations of the water and energy cycles, realistic representations of managed agroecosystems are essential to be included in LSMs. The surface energy balance and water use for each crop type is unique, as their physiology and phenology are distinct from one another. As such, crop growth directly affects surface energy partitioning into sensible and latent heat flux, as well as local and regional surface water balances (e.g., Twine et al. 2004; Mishra et al. 2010; Lu et al. 2015). In addition, changes in crop management such as planting dates and adoption of new cultivars have had significant impacts on greening/growth time and subsequently seasonal energy budgets and hydrologic trends (Sacks and Kucharik 2011). Results of these studies emphasize the importance of including accurate descriptions of crop dynamics in land surface models in order to improve simulations of seasonal energy and water fluxes and thus regional climate, particularly in the central United States.

While previous studies have focused on hydroclimatic trends on annual time scales (e.g., Greve et al. 2014), specific seasons (e.g., Portmann et al. 2009), select variables (e.g., Sacks and Kucharik 2011), and/or at specific locations (e.g., Burns et al. 2007), a comprehensive assessment of the spatiotemporal variability of the regional water balance in the central United States remains lacking—particularly a full geospatial understanding of how changes in water availability within a given season affect water budget trends in succeeding seasons.

In this study, we use the agricultural version of the Integrated Biosphere Simulator (Agro-IBIS) model—a dynamic vegetation and land surface hydrologic model incorporating growth and management of corn, soybeans, and wheat (Kucharik et al. 2000; Kucharik 2003; Kucharik and Brye 2003)—to simulate variations in the surface water balance of the central United States from 1984 to 2007. Details and advantages of the model, including model validation, are referenced and described in section 2, and components of the terrestrial water cycle are analyzed in subsequent sections. From these analyses, we gain a new understanding of how various surface water budget terms work synchronously or asynchronously with seasonal lags in determining annual and seasonal hydrologic trends, as well as how these trends are associated with local and regional climate variability across the study region. We also gain a better understanding of distinctive responses in the water cycle within specific regions of managed (crop) and natural land cover.

We use high-resolution meteorological data to force the model, and we also provide model validation to ensure that the model outputs are temporally and spatially reliable (Elguindi et al. 2011). These data are described in section 3. In section 4, we discuss variations in the regional surface water balance in terms of the long-term mean, seasonal variability, and trends. Results of this work are summarized in section 5 in the context of advancing our understanding of hydroclimatic change and its impact on central U.S. agriculture. Limitations of this study such as the absence of anthropogenic water management and its impacts on our results are addressed.

2. Agro-IBIS model and its implementation

The Agro-IBIS model (Kucharik 2003; Kucharik and Brye 2003) is an advanced version of IBIS, a dynamic global vegetation model that integrates hydrologic, physiological, biophysical, and ecological processes into a single, consistent framework (Foley et al. 1996; Kucharik et al. 2000). The model has been used extensively to investigate energy, water, and carbon balances in soil–vegetation–atmosphere systems on local, regional, and global scales (e.g., Lenters et al. 2000; Twine et al. 2004; Mykleby et al. 2016). Because Agro-IBIS incorporates crop management, crop phenology, belowground carbon/nitrogen cycling, and solute transport, it is capable of simulating both natural and managed ecosystems of various crops (e.g., maize, soybeans, and spring and winter wheat) across the United States. While most LSMs represent crops as unmanaged “grass-like” C3 or C4 plant functional types (PFTs) that are modified to have phenology match typical crop growth (e.g., Lawrence et al. 2012), Agro-IBIS explicitly simulates the planting, growth, and harvest of crops with algorithms based on specific crop physiology and phenology that are also responsive to various management options. Carbon allocation is split among a variety of pools (leaf, stem, root, and grain), and leaf emergence, grain fill, and harvest are determined by the summations of growing degree days (GDDs). With the ability to account for shifts in planting dates and selection of hybrids in a changing climate, the model simulates more realistic climate impacts on vegetation processes and water balance. For brevity, only major aspects of the model are described here. Details about agriculture-related processes in the model are described in Kucharik (2003), Donner and Kucharik (2003), and Kucharik and Brye (2003).

In terms of the surface water balance, Agro-IBIS calculates ET as the sum of three components: evaporation of water intercepted by vegetation, evaporation from the soil surface, and plant transpiration. Soil evaporation rates are calculated based on the standard mass transfer equation, which is a function of temperature, vapor pressure deficit, and atmospheric conductance (Campbell and Norman 1998). Transpiration rates are calculated independently for each PFT by taking into account plant physiological parameters such as leaf area index and stomatal resistance. Total runoff in Agro-IBIS is calculated as the sum of surface runoff and drainage. When rainfall occurs, precipitation (including melting snow) is apportioned between surface runoff and puddle water/ice. Some portions of the puddle liquid go to evaporation, while some are transferred into infiltration until the soil is saturated. Any excess puddle liquid becomes surface runoff. The version of Agro-IBIS used in this study does not specify groundwater as a lower boundary, but rather allows free drainage to occur and contribute to total runoff. However, recent improvements to Agro-IBIS have added an explicit representation of groundwater and the inclusion of HYDRUS-1D (a process-based vadose zone model) to improve the representation of soil physics by allowing the model to simulate variably saturated soil water flow (Soylu et al. 2014).

Agro-IBIS and its preceding versions have been extensively evaluated and validated for simulating the terrestrial surface water balance at various spatial and temporal scales. Kucharik et al. (2000) showed that the IBIS-simulated spatial patterns of climatological runoff from 1965 to 1994 accurately characterize observed river gauge statistics at the continental scale. Lenters et al. (2000) showed that IBIS simulations of seasonal and interannual variations in ET, runoff, soil moisture, and snow depth are in general agreement with observations across the continental United States. In snowy regions, such as northern Wisconsin, water and energy cycling simulated by Agro-IBIS at daily to interannual time scales also exhibit reasonable accuracy (Vano et al. 2006). At site-specific scales, soil temperature, moisture, and energy fluxes modeled by Agro-IBIS compare well with eddy covariance measurements and other crop biophysical data from the AmeriFlux site in Mead, Nebraska (Kucharik and Twine 2007). At an observation site in the Republican River basin in Nebraska and Kansas, Agro-IBIS has been used to accurately model seasonal ET in a wetland dominated by Phragmites australis (Mykleby et al. 2016).

In addition to these previous evaluations of the Agro-IBIS model performance in simulating the surface water balance, we have further compared the Agro-IBIS simulated runoff against USGS river discharge data in the Mississippi River basin. In the absence of a river routing scheme in the model, the timing of our basinwide aggregated runoff is expected to be earlier than gauged streamflow. And because of the absence of land surface manipulation, human consumption, water management (e.g., Fort Peck and Garrison Dams), and ground-surface water interaction in the model (Dong 2012), simulated summer ET in water-limited, farming-intensive basins (e.g., the Missouri River basin) is expected to be underestimated. Nevertheless, the interannual variability of the Agro-IBIS simulated runoff compares reasonably well with observations (Fig. A1 in appendix), and none of the basins show significant trends in runoff during our study period, a result consistent with observed streamflow trends (except for the Missouri basin, which shows −0.007 mm day−1 yr−1 in USGS gauge data). These assessments justify our continued use of Agro-IBIS for studying climate impacts on the terrestrial water cycle of the central United States.

3. Data and methods

a. Data

In this study, daily meteorological data from 1984 to 2007 (provided by ZedX, Inc.) are used as climatic inputs to drive the Agro-IBIS model. Daily precipitation, relative humidity, wind speed, and maximum and minimum air temperature from station observations are interpolated to model grids and fed into the model simulations. The model grid resolution is 0.083° × 0.083° (5 min, approximately 8 km × 8 km) over the contiguous United States and southern Canada. To be compatible with the Agro-IBIS 60-min time step, the daily data are transposed into hourly data using a weather generator (Richardson 1981; Richardson and Wright 1984). Surface solar radiation inputs are obtained from the NASA Global Energy and Water Cycle Experiment (GEWEX) Surface Radiation Budget (SRB) shortwave radiation dataset version 3.0 (obtained from the NASA Langley Research Center Atmospheric Science Data Center, at http://gewex-srb.larc.nasa.gov), superimposed with higher-resolution spatial anomaly maps from the North American Regional Reanalysis (NARR) downward shortwave radiation (Mesinger et al. 2006). The SRB solar radiation data are modified in this way in order to match the spatiotemporal resolution of the other input variables, while also avoiding known biases in the NARR dataset. We further compared our forcing data with meteorological data in NLDAS2 (Xia et al. 2012) and data provided by Livneh et al. (2013). The results show consistent trends for most variables across our study region (see online supplemental material).

Crop cover data were obtained from Monfreda et al. (2008) and Ramankutty et al. (2008) by merging two satellite products from Boston University’s MODIS land cover dataset and the Global Land Cover 2000 (GLC2000) dataset. The data (MR2008 hereafter) are supplied at 5-min spatial resolution and provide information on fractional coverage of 175 global crop cover types, based on observations from the year 2000. In the study domain, besides natural vegetation, maize and soybeans are considered as the two dominant types of managed land cover. Natural vegetation data are obtained from Ramankutty and Foley (1999) and include 15 potential vegetation classes. Soil texture data are derived from the USDA State Soil Geographic Database (Miller and White 1998) and are spatially transposed from the original 1 km × 1 km resolution to the 5-min grid.

b. Model simulation design

In this study, the Agro-IBIS model was run for 24 years from 1984 to 2007. Prior to the 24-yr simulation, the model was spun up for 5 years, forced by climate inputs from 1984 (with the effects of nitrogen stress on crop growth turned off). The model domain encompasses four major basins: the Missouri River, Upper Mississippi River, Ohio River, and the Great Lakes basin (Fig. 1b). Before producing the final simulation, Agro-IBIS was executed three times with three different land cover types: one with natural vegetation, and the other two with maize or soybeans as the only vegetation types covering the entire model domain. Outputs from the three independent simulations were then combined through a weighted average, according to the actual fractional coverages of natural vegetation, maize, and soybeans within each grid cell (Fig. 1). The intent of running three separate single-land-cover simulations is to produce the most realistic land surface scenario that matches the actual land cover in each grid cell, which is composed primarily of natural vegetation and the two crop types. Although no other vegetation types (Fig. 1d) are explicitly included in this set of simulations, the fractional coverage by other types is generally low throughout model domain except in the northwestern and southern Missouri River basins, where winter and spring wheat are cultivated, and the intensity of their water cycle is not significantly different from natural vegetation (Twine et al. 2004). Outputs from the Agro-IBIS simulations include ET, total runoff, soil volumetric water content (VWC), snow depth, and snowfall (in terms of snow water equivalent), all of which are calculated as monthly mean values from daily outputs.

Fig. 1.
Fig. 1.

Percent area of each grid cell covered by (a) natural vegetation, (b) maize, (c) soybean, and (d) other crops based on the MR2008 dataset. Basin boundaries are delineated by bold black lines and labeled in (b).

Citation: Journal of Hydrometeorology 21, 9; 10.1175/JHM-D-19-0050.1

c. Surface water balance

The land surface water balance is calculated according to (e.g., Shelton 2009)

dW/dt=PETR,

where dW/dt is the rate of change in the total terrestrial water storage (including soil liquid water, soil ice, snow, and groundwater), P is precipitation, ET is evapotranspiration, and R is the total runoff. On the regional scale, R and dW/dt are considered to be primary indicators of freshwater availability, with P and ET representing source and sink terms, respectively. Accordingly, we reformulate (1) to

R+dW/dt=PET.

d. Trend analysis

To estimate changes in hydroclimatic variables across the study region, we applied a linear trend analysis on historical climate and Agro-IBIS simulated water balance data from 1984 to 2007. According to the past century-long observations (supplemental material), this period is not biased as a significant wet or dry epoch. We calculated trends as Sen’s slopes using the Thiel–Sen approach (Thiel 1950; Sen 1968). Unlike simple linear regression, this method has the advantage of eliminating the influence of outliers in calculated trends. Statistical significance of the trends is determined using the Mann–Kendall (MK) test (Mann 1945; Kendall 1975). The MK test is nonparametric, such that it does not rely on assumptions of normality, linearity, nor independence of the data. Because hydrologic processes such as streamflow and precipitation do not typically exhibit a normal distribution, the MK test is more appropriate than other tests for our trend analysis (e.g., Yue et al. 2002; Zhang et al. 2009). It is important to note that the calculated trends in our analysis are not intended to represent long-term climate change, which would typically require several decades of data for detection (Lettenmaier and Burges 1978; Kundzewicz and Robson 2000). Rather, we are using the trend results to quantify the overall direction and magnitude of change in the regional water balance during the 24-yr study period.

4. Results

a. Background trends in climatic variables

Changes in annual mean climatic conditions in the central United States from 1984 to 2007 show increases in annual average air temperature at a rate of approximately 0.3°–1.0°C decade−1 (Fig. 2a). The warming rate is especially steep in the Great Lakes, Upper Mississippi, and southwestern Missouri River basins. In addition, daily minimum air temperature has increased faster than daily maximum air temperature, resulting in a narrowing trend in diurnal temperature range (DTR), particularly in the north-central United States (Fig. 2b). Annual precipitation shows considerable spatial heterogeneity in its trend throughout the study region, but with significant upward trends in portions of Ohio, Indiana, Oklahoma, and North Dakota (Fig. 2c). Annual mean relative humidity (RH) has increased over much of the study domain (Fig. 2d), with statistically significant trends in regions that coincide with observed increases in annual precipitation. The upward RH trends are also physically consistent with the narrowing DTR trend, since humid air tends to reduce nocturnal surface heat loss by increasing downward longwave radiation from the atmosphere.

Fig. 2.
Fig. 2.

Sen’s slope trends (1984–2007) in annual mean (a) air temperature, (b) diurnal temperature range, (c) precipitation, (d) relative humidity, (e) incoming solar radiation, and (f) wind speed. Trends that are statistically significant at the 90% confidence level are hatched. The bold black line encompasses the primary study region.

Citation: Journal of Hydrometeorology 21, 9; 10.1175/JHM-D-19-0050.1

Despite increases in annual precipitation, analysis of satellite remote sensing data has shown that the central United States experienced an overall “brightening” in annual mean incoming solar radiation from 1984 to 2007 (Fig. 2e). This brightening suggests a decrease in cloud cover (and/or aerosols) during at least a portion of the annual cycle (explored later; Tollenaar et al. 2017). The brightening trend, together with the warming, implies an increase in available energy at the surface (i.e., “available” for sensible and latent heat flux) over the past three decades. Given sufficient water availability, this increasing available energy would intensify the water cycle by increasing ET (also a source for the upward trend in RH). This process may have been counteracted, to some extent, by a simultaneous decrease in near-surface wind speed throughout much of the study domain (Fig. 2f, except in the western Missouri River basin). This “stilling” phenomenon (Roderick et al. 2007) partially restrains the rise of ET by reducing evaporative demand.

We further dissect some of the annual mean trends (here and in later sections) by showing trends in climatic variables for specific seasons, starting with changes in winter (DJF) precipitation. Figure 3a shows that winter liquid precipitation has increased in the central United States, while snowfall (Fig. 3b) has remained unchanged or even declined in some areas such as the northern Great Lakes and southwestern Missouri River basins. In the northern Great Lakes basin, winter snow depth has dropped significantly (Fig. 3c), a change consistent with the downward trend in snowfall (Fig. 3b). Such changes indicate that, from the beginning to the end of the 24-yr study period, an increasing fraction of winter precipitation has fallen as rain, consistent with the findings of Feng and Hu (2007). This change, as a result of warmer winter air temperatures, has strong implications for surface hydrology in winter and spring (detailed in sections 4d and 4e). Other seasonal trends in precipitation, temperature, and solar radiation are discussed in conjunction with trends in the surface water balance (sections 4cf). Seasonal trends in other climatic variables (i.e., wind speed and RH) generally follow annual trends similar to those that have already been discussed, unless noted otherwise in later sections.

Fig. 3.
Fig. 3.

Sen’s slope trends in winter mean (DJF) (a) rainfall, (b) snowfall, and (c) snow depth from 1984 to 2007. Trends that are statistically significant at the 90% confidence level are hatched.

Citation: Journal of Hydrometeorology 21, 9; 10.1175/JHM-D-19-0050.1

b. Mean seasonal water balance in different basins

The modeled long-term monthly mean water balance for 1984–2007 is shown in Fig. 4 for each of the four study basins, illustrating a few common characteristics and differences among basins. In winter months, simulated ET rates are generally low. Because winter precipitation is considerably higher than ET, particularly in the Ohio River basin, dW/dt > 0, indicating increases in soil water storage in winter (and/or accumulation of snow in colder regions). In spring, both ET and runoff increase, as does precipitation in the Missouri and Upper Mississippi River basins; dW/dt becomes negative, a change indicating loss of snow and soil water storage. In summer, ET > P as a result of abundant solar energy available for ET. Consequently, runoff decreases compared to that in spring, and soil water deficits increase. As precipitation and available energy decline in fall, ET decreases rapidly, and dW/dt > 0 marks the start of another annual hydrologic cycle.

Fig. 4.
Fig. 4.

Mean seasonal cycle of the surface water balance for the (a) Missouri, (b) Upper Mississippi, (c) Ohio, and (d) Great Lakes basins. Each mark on the lines denotes basin averaged long-term monthly mean (1984–2007) observed precipitation (dotted medium gray with circles) and Agro-IBIS simulated ET (black solid with diamonds), runoff (dotted light gray with inverted triangles), and dW/dt (dotted black with triangles). Error bars indicate the interannual standard deviation (1984–2007).

Citation: Journal of Hydrometeorology 21, 9; 10.1175/JHM-D-19-0050.1

While following this average seasonal cycle, surface water balance has notable differences among the four basins (Fig. 4; maps of full spatial characteristics of the seasonal water balance are presented as Figs. A2A5 in the appendix). The seasonal cycle of precipitation is much more pronounced in the two western basins (Missouri and Upper Mississippi) than in the two eastern basins (Ohio River and the Great Lakes). Similarly large amplitudes in the ET seasonal cycle in the two western basins create a relatively muted seasonal cycle for runoff, especially in the Missouri River basin. The timing of peak runoff varies considerably among the basins, from March in the Ohio River basin to May in the Great Lakes and Upper Mississippi River basins to June in the Missouri River basin. The most pronounced seasonal cycle of runoff is found in the Great Lakes basin, where the maximum snow depth is largest (Fig. 5). The large amount of snow received during the lake-effect snow season from November to February contributes to the snow-dominant hydrology in the basin. In contrast, shallower snow depths and a warmer climate in the Ohio River basin lead to an earlier and smaller spring peak in the annual hydrograph.

Fig. 5.
Fig. 5.

Mean seasonal cycle of Agro-IBIS simulated cold-season (November–April) snow depth for the four river basins. Each bar denotes the basin-averaged long-term monthly mean (1984–2007) snow depth. Error bars indicate the interannual standard deviation.

Citation: Journal of Hydrometeorology 21, 9; 10.1175/JHM-D-19-0050.1

Other characteristics of the long-term mean surface water budget components for each month are also shown in Fig. 4. Precipitation has the largest interannual fluctuation (i.e., standard deviation) among all budget terms, especially during the local wet season (summer in the two western basins). In contrast, ET has the least interannual variability, despite the large variability in precipitation. This result suggests that ET is predominantly energy limited in these regions, with soil moisture and available energy acting to buffer ET from large variations in precipitation. Energy limitation of ET is also evident in the seasonal water cycle of the Ohio River and the Great Lakes basins (Figs. 4c,d). ET in those basins shows large seasonal variations despite a much weaker seasonal cycle of precipitation. However, variations in precipitation are found to have a strong influence on water storage (soil water and snow cover), as shown by similarly large fluctuations in dW/dt. Interannual variations in snow depth are large compared to the seasonal mean (Fig. 5), with the largest snow depth variability in the Great Lakes basin in February and March. In addition, these large fluctuations in snow depth coincide with large fluctuations in cold-season dW/dt, but small variations in runoff (Fig. 4d). These results suggest an important role of soil water storage in buffering variations in spring runoff from snowmelt.

c. Changes in the annual surface water balance

Changes in the annual and seasonal surface water budget of the central United States from 1984 to 2007 are shown in Figs. 610 (calculated as Sen’s slope trends). Note that we show trends in −ET (instead of +ET) in order to follow the color convention of the figures, wherein blue (red) indicates increased (decreased) water availability. Overall increasing trends in annual ET are shown throughout much of the model domain (Fig. 6b), suggesting decreased water availability and increased surface water stress over much of the study area. This result is consistent with the increasing atmospheric RH previously shown in Fig. 2d. Unlike ET, however, trends in annual mean runoff in the four basins are more heterogeneous in the sense that areas of negative and positive trends are comparable (Fig. 6c). Significant positive trends in runoff are found in the Ohio River and northern Missouri River basins, and negative trends in the northern Great Lakes and southern Missouri River basins. This pattern is largely in accordance with changes in precipitation (Fig. 6a), which is a dominant factor in determining the spatial variability of changes in annual mean runoff. Because the long-term annual mean rate of surface water storage is near zero (Fig. 6d), these trends indicate an intensified annual surface water cycle across most of the central United States, with changes in precipitation mapping onto changes in runoff, superimposed upon an overall upward trend in annual mean ET.

Fig. 6.
Fig. 6.

Sen’s slope trends in components of the annual mean surface water budget (1984–2007), including (a) observed precipitation and Agro-IBIS simulated (b) ET (plotted as −1 × ET), (c) runoff, and (d) the rate of change in soil water storage (dW/dt). Trends that are statistically significant at the 90% confidence level are hatched.

Citation: Journal of Hydrometeorology 21, 9; 10.1175/JHM-D-19-0050.1

Fig. 7.
Fig. 7.

(a)–(d) As in Fig. 6, but for the winter season (DJF). (e),(f) The trends in observed DJF temperature and incoming solar radiation, respectively.

Citation: Journal of Hydrometeorology 21, 9; 10.1175/JHM-D-19-0050.1

Fig. 8.
Fig. 8.

As in Fig. 7, but for the spring season (MAM).

Citation: Journal of Hydrometeorology 21, 9; 10.1175/JHM-D-19-0050.1

Fig. 9.
Fig. 9.

As in Fig. 7, but for the summer season (JJA).

Citation: Journal of Hydrometeorology 21, 9; 10.1175/JHM-D-19-0050.1

Fig. 10.
Fig. 10.

As in Fig. 7, but for the autumn season (SON).

Citation: Journal of Hydrometeorology 21, 9; 10.1175/JHM-D-19-0050.1

d. Changes in winter surface water balance

On seasonal time scales, the spatial pattern of changes in winter precipitation (Fig. 7a) matches (pattern correlation = 0.73) changes in the rate of surface water storage (Fig. 7d) more closely than changes in runoff (Fig. 7c). Meanwhile, trends in ET are weak or slightly decreasing in some regions (Fig. 7b). Some of the negative trends in ET coincide with decreases in winter-mean wind speed (not shown), such as in the Upper Mississippi basin. Decreases in ET in water-limited regions, such as the western Missouri basin (Fig. 7b), are associated with decreasing precipitation (Fig. 7a). On the other hand, in energy-limited regions such as the northern Great Lakes, strong declines in winter precipitation (Fig. 7a) and snowfall (e.g., north of Lake Superior; Fig. 3b) result in loss of snow water storage (Figs. 3c and 7d), but have limited effects on winter runoff (Fig. 7c) and ET (Fig. 7b). In the Upper Mississippi River basin, declines in winter runoff (Fig. 7c) reflect increases in soil water storage (Fig. 7d). This increased winter water storage occurs after a significant decline in fall precipitation and dW/dt (further discussed in Fig. 10), suggesting an important role for seasonal lags in soil water replenishment, with impacts on seasonal runoff.

e. Changes in spring surface water balance

In terms of the regional climate, Fig. 8e shows some evidence of colder spring air temperatures (particularly in the northern Missouri River basin), in contrast to the overall warmer annual mean air temperatures (Fig. 2a). This cooling trend occurs in concert with increased precipitation (Fig. 8a; not shown outside of the basin boundary) and reduced solar radiation (Fig. 8f). The latter suggests an increase in cloud cover during the spring. Precipitation has increased in general (Fig. 8a), along with increases in the rate of surface water storage (Fig. 8d). These changes result in a more complex pattern of changing runoff (Fig. 8c). In contrast to the increases in water storage (Fig. 8d), soil moisture (Fig. 11b) does not show significant trends in the northern Great Lakes basin. Thus, the storage change is mainly a consequence of reduced snowmelt. A few areas show weak but statistically significant increases in ET (Fig. 8b), whereas few significant trends are found in incoming solar radiation (Fig. 8f) and surface air temperature (Fig. 8e).

Fig. 11.
Fig. 11.

Sen’s slope trends in the seasonal-mean volumetric water content (0–2.5-m soil depth), as simulated by the Agro-IBIS model for 1984–2007 during (a) DJF, (b) MAM, (c) JJA, and (d) SON.

Citation: Journal of Hydrometeorology 21, 9; 10.1175/JHM-D-19-0050.1

The most prominent changes in spring water balance are found in the northern Great Lakes basin, where strong decreases in runoff (Fig. 8c, 0.4–0.6 mm day−1 decade−1) occur with increased precipitation (Fig. 8a) and much larger increases in the rate of surface water storage (Fig. 8d, 0.7–0.8 mm day−1 decade−1). The large, positive trend in dW/dt reflects a significant decrease in water loss (i.e., snowmelt), rather than an increase in local water accumulation, and it is precisely this decrease in snowmelt that has resulted in the strong decline in spring runoff. This result is anticipated in regions of decreasing winter snow depth (Fig. 3c), where spring snowmelt would also be expected to decrease. With the observed declines in fall and winter volumetric water content (Figs. 11a,d), it is also conceivable that the upward trends in dW/dt are due, in part, to an increasing portion of the snowmelt being utilized to recharge the drier soil, rather than contributing to surface runoff.

f. Changes in summer surface water balance

Multidecadal changes in the surface water balance during summer (JJA, Fig. 9) show large differences from that in spring and winter. Changes in summer precipitation are characterized by significant declines in portions of the Great Lakes basin, but increases in parts of the Upper Mississippi and Ohio River basins. With plenty of available energy (high solar insolation), these areas of increased (decreased) precipitation generally align with regions of increased (decreased) ET (Figs. 9a,b). The trend in dW/dt (Fig. 9d) follows a pattern similar to precipitation in the Great Lakes basin. As a result of these changes in ET and dW/dt, changes in summer runoff (Fig. 9c) are not as widespread as changes in precipitation, showing only small increases across some parts of the study region. In those areas, significant reductions in precipitation (Fig. 9a) occur in conjunction with strong solar brightening (Fig. 9f) and increases in surface air temperature (Fig. 9e), leading to increased available energy for ET (Fig. 9b). Areas of increase or decrease in dW/dt in summer (Fig. 9d) show similar trends in seasonal-mean soil water content (Fig. 11c), such as significant drying in the northern Great Lakes basin.

g. Changes in autumn surface water balance

Changes in the autumn (SON) surface water balance are shown in Fig. 10. In contrast to winter and spring, strong trends in precipitation (Fig. 10a) and ET (Fig. 10b) are evident in large parts of the domain, with much of the increase in ET associated with prominent solar brightening (Fig. 10f). In the lower Great Lakes basin, on the other hand, ET does not show significant increases as a result of reduced available water (i.e., decreases in precipitation). Compared to summer, some fall precipitation trends are not compensated for by similar trends in ET, but rather by changes in the rate of water storage (Fig. 10d). For example, significantly lower rates of dW/dt are found in lower Michigan and southern portions of the Missouri and Upper Mississippi River basins as a result of reduced autumn precipitation. Decreases in autumn runoff (Fig. 10c) and VWC (Fig. 11d) are also prevalent across much of the Great Lakes basin.

Aside from the far western Missouri River basin, incoming solar radiation at the surface during fall has increased significantly over the past few decades across much of the central United States (Fig. 10f). This strong solar brightening coincides with reduced precipitation (Fig. 10a) and dW/dt (Fig. 10d) in central portions of the domain, along with reduced fall runoff in the lower Great Lakes basin (Fig. 10c). These changes also occur in concert with warmer autumn (Fig. 10e) and annual mean (Fig. 2a) surface air temperatures, and increases in autumn DTR in central portions of the study domain (Fig. A6 in appendix).

h. Water budget trends in natural vegetation and managed crop lands

While our water balance trend analyses are based on land cover which includes both natural and managed vegetation, it is important to know if natural vegetation and managed croplands have significantly different water cycle responses to climate variations. To investigate this, we repeated the same analyses for simulations with natural vegetation, maize, and soybeans separately. In general, annual trends in ET (Figs. 12a–c) are broadly similar in spatial pattern across the basins (pattern correlations > 0.7). This result suggests that although crops such as maize and soybeans have more intense mean water cycles than natural vegetation cover (Twine et al. 2004), their responses to climate shifts during our study period are largely consistent. Otherwise, we would have expected to see differences in trends between crops and natural vegetation lands that show a “Corn Belt” pattern. Nonetheless, despite the broad resemblance, there are many differences in ET at small scales. For instance, in the central Upper Mississippi River basin, increases in ET show larger spatial extent than simulations with natural vegetation or soybeans. This result indicates that without the inclusion of maize in the model the ET trend would be underestimated in the upper Mississippi River basin. Over the northern tip of the Great Lakes basin, there is no ET trend in simulation for soybeans, whereas a decreasing trend in ET is shown in the simulation with natural vegetation. These results suggest that in this study area, if land were converted to soybean cultivation, ET would not have changed from 1984 to 2007.

Fig. 12.
Fig. 12.

Trends (mm day−1 decade−1) in (top) annual ET (plotted as −1 × ET) and (bottom) runoff from 1984 to 2007 in the Agro-IBIS (a),(d) natural vegetation, (b),(e) maize, and (c),(f) soybean simulations. Shading with (without) hatching indicates significant negative (positive) trends.

Citation: Journal of Hydrometeorology 21, 9; 10.1175/JHM-D-19-0050.1

The response of runoff to climate shifts exhibits different behavior between simulations with managed croplands and natural vegetation lands. Figures 12d–f show that the Upper Mississippi River runoff trend is smaller in simulations with croplands than that with natural vegetation. In the southern part of the basin, runoff even shows negative trends in simulations with maize and soybeans. Differences also appear in the Ohio River basin, northern Missouri River basin, and northern Great Lakes basin. These results suggest that without maize and soybeans in the model’s land cover, runoff trends in response to past climate shifts would have been overestimated. Similar differences among the simulations also show up in different seasons (see seasonal trend figures in the supplemental material).

i. Differences between decadal and longer-term trends

We compared the Agro-IBIS simulated hydroclimate trends with NLDAS2 (Xia et al. 2012) and data from Livneh et al. (2013), both of which have outputs of ET and runoff across an extended temporal record. From 1984 to 2007, ET (Fig. 13) does not show significant trends except in the Agro-IBIS model, with a trend in the Ohio River basin of 10.4 mm yr−1 decade−1 and Upper Mississippi River basin of 10.6 mm yr−1 decade−1. NLDAS2 also shows a trend in Great Lakes basin ET of 11.9 mm yr−1 decade−1. These differences could arise from either differences in forcing data (more discussion in supplemental material) or model differences, or both. Runoff (Fig. 14) shows consistency among the datasets, with no significant trend during the study period. However, for longer time periods, both the data of Livneh (1915–2011) and NLDAS2 (1979–2018) show upward trends in ET in all basins. A similar upward long-term trend is also seen in runoff (Fig. 14), except that the NLDAS2 runoff shows no trend in the Ohio and Upper Mississippi River basins. The runoff trends from 1984 to 2007 also are different from the 40-yr streamflow trends reported by Lettenmaier et al. (1994), who showed significantly increasing trends for most of the Upper Mississippi and lower Great Lakes basins. These results indicate that short-term hydroclimate variations can exhibit quite different behavior from secular long-term trends. It also suggests that for studies of decadal hydroclimatic change, the choice of beginning and ending years of study should be made with caution, as it is possible that the time span coincides with a trending section of natural climate variation, or a dry or wet epoch such as the 1930s Dust Bowl.

Fig. 13.
Fig. 13.

Time series of annual ET (mm) from Livneh (light gray), NLDAS2 (medium gray), and the Agro-IBIS model results (black), averaged over each of the four different river basins.

Citation: Journal of Hydrometeorology 21, 9; 10.1175/JHM-D-19-0050.1

Fig. 14.
Fig. 14.

Time series of annual runoff (mm) from Livneh (light gray), NLDAS2 (medium gray), and the Agro-IBIS model results (black), averaged over each of the four different river basins.

Citation: Journal of Hydrometeorology 21, 9; 10.1175/JHM-D-19-0050.1

5. Discussion and conclusions

Spatiotemporal characteristics and trends in the hydrologic cycle of major river basins of the central United States have been examined in this study using the Agro-IBIS terrestrial biosphere model forced by observed climatic conditions from 1984 to 2007. Results show that the seasonal cycle of the surface water balance exhibits distinct spatial variations among the basins, with some regions showing a strong influence on runoff from trends in precipitation and ET, while other regions are more dominated by changes in snow and soil water storage (especially in northern parts of the study domain). Significant regionwide precipitation (0.029 mm day−1 decade−1) and ET trends (0.027 mm day−1 decade−1) averaged over the study domain suggest an overall acceleration of the terrestrial water cycle in the central United States. The modeled upward trends in ET are consistent with increases in available energy from higher air temperature and increased solar radiation, as well as reduced water limitation from higher precipitation (and higher relative humidity which is supported by the higher ET).

The hydrologic changes in the study region from 1984 to 2007 differ from longer, centennial-scale trends reported in prior studies (e.g., Lettenmaier et al. 1994; Groisman et al. 2004; Walter et al. 2004; Mo and Lettenmaier 2018), and discrepancies between these long-term trends and our 24-yr trends are partly due to the differences in targeted time scales (in part a reflection of the limited availability of high-quality climate forcing datasets prior to 1984). Strictly speaking, trends in the regional water cycle found in this study reflect not only recent climate change but also temporal segments of multidecadal or longer time scale variations in the climate system, such as the AMO and IPO (e.g., Enfield et al. 2001; Dong and Dai 2015, 2017; Dong et al. 2018; Mo and Lettenmaier 2018; Zhao et al. 2018). Centennial-scale trends identified in previous studies, while informative, reflect changes over periods that often contain several such decadal-scale segments examined in the present study. These interdecadal variations, on the other hand, reflect internal oscillations of the climate system superimposed on secular hydroclimatic trends (largely driven by external forcing from solar irradiance, volcanic activity, and anthropogenic aerosol loadings and greenhouse gas forcing; Dong and Dai 2017). While understanding the centennial-scale trends is important, additional quantification of interdecadal changes is also of considerable societal value for near-term water policy and planning purposes, as well as understanding the agricultural and economic impacts.

Our results show significant changes in the regional hydrologic cycle of the central United States in response to climatic trends from 1984 to 2007, with important seasonal differences and temporal lags. Winter and spring precipitation have generally increased, reflected mostly as a significant increase in rainfall, but with reduced winter snowfall in some regions (particularly the northern Great Lakes) as a result of warming winter temperatures. The wetter conditions have resulted in increased model runoff in some regions (e.g., the Ohio River basin), while other areas have seen increased soil water storage during winter and spring (e.g., southern Great Lakes and Upper Mississippi River basins). Summer patterns show a patchwork of drier conditions in the northern Great Lakes and wetter conditions to the south, with the impact on runoff and soil water storage being muted somewhat by partially compensating trends in ET during the (mostly) water-limited summer season. Significant solar brightening during summer and fall, particularly in the Great Lakes basin, compounds some of the drier conditions and contributes additional available energy for ET. Reductions in fall precipitation are also more expansive than in summer, stretching from the north-central Great Lakes to the southern boundary of the study domain, along with significant soil drying. This model result is consistent with the drought physics discussed in Livneh and Hoerling (2016), namely, that rainfall deficits are shown to be primarily responsible for soil moisture depletion in their LSM simulations. Similar to other seasons, the impact of reduced fall precipitation on runoff is muted by reductions in soil water storage, rather than direct changes in runoff. This soil drying trend during fall, however, eventually results in a decline in runoff during winter in southern portions of the Upper Mississippi and Missouri River basins, with the aforementioned increases in winter rainfall leading to replenishment of soil water storage, rather than increases in runoff. These seasonal leads and lags in the regional surface water balance trends highlight the importance of considering the within-season changes not in isolation from other seasons, but in concert with one another.

Among seasons, spring shows the largest decrease in runoff in the northern Great Lakes basin, where snowmelt constitutes a large part of the spring (as well as annual) runoff. This decreasing trend is the result of declining snow accumulation in recent decades because of proportionally more precipitation falling in liquid form as the climate warms in the Great Lakes basin. This warming from 1984 to 2007 is especially evident during fall and winter, not just in the Great Lakes region but over the entire study domain. Along with the declining winter snowfall trend, there has been evidence of contraction in early spring snow cover extent and earlier dates of last snow on the ground in the Missouri River basin since 1950 (Groisman et al. 2001). As a result, the peak of snowmelt-induced runoff has gradually shifted earlier in the year, and with smaller amplitude. Although our seasonal analyses do not resolve these finescale temporal shifts in surface runoff, the significant downward trend in modeled winter snow depth in the northern Great Lakes basin (Fig. 3c) suggests similar reductions in peak spring runoff (Fig. 8c).

We found that the trend in the seasonal rate of terrestrial water storage is directly coupled to trends in precipitation, soil moisture, and snow cover, with changes in dW/dt leading to a substantially muted impact of precipitation on runoff. During fall, winter, and spring, trends in precipitation and snow depth dominate the trend in dW/dt, especially in the northern Great Lakes basin. In summer, the effects of precipitation changes on dW/dt are largely muted as a result of compensatory changes in ET. Apart from the winter snow–spring runoff relationship previously discussed, soil moisture plays an important role in these ET variations, which subsequently modify surface energy fluxes and the rate of water cycling between the land surface and the atmosphere. Because of the memory effect of the soils, such impacts usually persist and maximize in the following season. This memory effect also explains the “imbalanced” relationship between changes in seasonal precipitation and ET, such as in the Upper Mississippi basin, where fall ET has increased strongly regardless of a significant decline in precipitation (i.e., the ET is supported by prior soil wetting during summer). Soil moisture conditions and trends also affect changes in seasonal runoff in the subsequent season, since soil wetness determines the soil’s ability to hold additional amounts of infiltrated water from precipitation. This time-lag response of soil water storage, ET, and runoff to precipitation suggests that the state of terrestrial water storage in the preceding season must be carefully taken into account in order to holistically understand variations in the seasonal water cycle.

In response to climate trends during the 24-yr study period, water budget changes on managed croplands and natural vegetation lands were found to be different. ET trends from simulations with maize are slightly higher than simulations with soybean and natural vegetation in the central Upper Mississippi River basin. Runoff trends from simulations with either maize or soybeans are lower than simulations with natural vegetation in most of the central United States. These differences highlight the importance of representing managed croplands in LSMs in order to more accurately simulate hydrologic trends. Without crops, the water cycle intensification rate is likely to be overestimated. Regarding the impacts of agricultural cultivation and practice on variations in the regional hydrologic cycle, we note that maize production in the central United States has been shifting toward earlier planting dates, longer growing seasons, and adoption of new hybrids in order to adapt to springtime warming over the past few decades (Kucharik 2006; Lee and Tollenaar 2007). Consequences of these changes include increases in corn yield and ET in the early growing season (Kucharik 2008; Sacks and Kucharik 2011), and such increases have been simulated in the Agro-IBIS model. One uncertainty in the simulations could arise from the absence of land cover/land use shifts, such as recent conversion of forests and grasslands to croplands (Donner 2003; Twine et al. 2004), as well as changes from row crops to perennial bioenergy crops (e.g., switchgrass and miscanthus) in the Midwest (VanLoocke et al. 2010; VanLoocke et al. 2012; Wang et al. 2016) in order to support second-generation biofuels and the Renewable Fuel Standard. Therefore, the magnitudes of some hydrologic trends in our results should be interpreted with caution, especially near the margins of the U.S. Corn Belt.

Future advances in simulating and understanding the regional water cycle should include the effect of farming decisions and management practices on crop growth and the water cycle. Including such aspects in models would allow one to study and understand human interactions with the regional environment, as well as atmospheric circulation and climate (Hu et al. 2018). An important example of this is irrigation, which is heavily implemented in agriculture in the Missouri River basin but is not simulated in this study. This underrepresented consumptive water use in our model would likely result in an underestimation of the growing season ET and ET trends in the Great Plains (e.g., Milly and Dunne 2001; Mahmood and Hubbard 2004; Sacks and Kucharik 2011; Dong 2012; Lu et al. 2015). Also, in the absence of simulated water management practices such as dam operations, long-term runoff trends can be underestimated due to changes in regulated flow regimes to cope with climate change (Zhou et al. 2018). Equally important, but not included in our simulations are effects of advances in cultivating technology such as tillage and fertilizer use in cropping fields (Zhang and Schilling 2006), as well as rotation of soybean–maize cultivation (Song et al. 2013). These practices can introduce potential biases in estimating hydroclimatic trends (Liu et al. 2016), although their magnitudes may be small.

Acknowledgments

This work was partially supported by the National Science Foundation Innovations at the Nexus of Food, Energy, and Water Systems (INFEWS) program (Award 1855996) to the University of Wisconsin–Madison, and also by the USDA Cooperative Research Project NEB-38-088. This study was also supported by funding from the Nebraska Water Resources Advisory Panel (WRAP), the U.S. Department of Energy’s Office of Science through the Midwestern Regional Center of the National Institute for Climatic Change Research at the Michigan Technological University, the Institute of Agriculture and Natural Resources, University of Nebraska–Lincoln, NASA’s Glenn Research Center, and the Ohio Aerospace Institute.

APPENDIX

Hydroclimatology of the Central United States

This appendix contains a comparison of Agro-IBIS simulated runoff with USGS streamflow observations (Fig. A1), spatial characteristics of the climatological seasonal surface water balance of the central United States (Figs. A2A5 ), and observed seasonal trends in diurnal temperature range over the continental United States and southern Canada (Fig. A6).

Fig. A1.
Fig. A1.

Annual mean Agro-IBIS simulated runoff (gray) and observed annual mean streamflow from USGS (black; converted to basin-average runoff) for the (a) Missouri, (b) Upper Mississippi, (c) Ohio, and (d) southern Great Lakes basins.

Citation: Journal of Hydrometeorology 21, 9; 10.1175/JHM-D-19-0050.1

Fig. A2.
Fig. A2.

The 1984–2007 mean DJF (a) observed precipitation and Agro-IBIS simulated (b) ET (plotted as −1 × ET), (c) runoff, and (d) the rate of change in soil water storage (dW/dt).

Citation: Journal of Hydrometeorology 21, 9; 10.1175/JHM-D-19-0050.1

Fig. A3.
Fig. A3.

As in Fig. A2, but for the MAM season.

Citation: Journal of Hydrometeorology 21, 9; 10.1175/JHM-D-19-0050.1

Fig. A4.
Fig. A4.

As in Fig. A2, but for the JJA season.

Citation: Journal of Hydrometeorology 21, 9; 10.1175/JHM-D-19-0050.1

Fig. A5.
Fig. A5.

As in Fig. A2, but for the SON season.

Citation: Journal of Hydrometeorology 21, 9; 10.1175/JHM-D-19-0050.1

Fig. A6.
Fig. A6.

Sen’s slope trends in SON diurnal temperature range from 1984 to 2007. Cross (line) hatching indicates significant positive (negative) trends.

Citation: Journal of Hydrometeorology 21, 9; 10.1175/JHM-D-19-0050.1

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